{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Distillation drift detector on CIFAR-10\n",
    "\n",
    "### Method\n",
    "\n",
    "[Model distillation](https://arxiv.org/abs/1503.02531) is a technique that is used to transfer knowledge from a large network to a smaller network. Typically, it consists of training a second model with a simplified architecture on soft targets (the output distributions or the logits) obtained from the original model. \n",
    "\n",
    "Here, we apply model distillation to obtain harmfulness scores, by comparing the output distributions of the original model with the output distributions \n",
    "of the distilled model, in order to detect adversarial data, malicious data drift or data corruption.\n",
    "We use the following definition of harmful and harmless data points:\n",
    "\n",
    "* Harmful data points are defined as inputs for which the model's predictions on the uncorrupted data are correct while the model's predictions on the corrupted data are wrong.\n",
    "\n",
    "* Harmless data points are defined as inputs for which the model's predictions on the uncorrupted data are correct and the model's predictions on the corrupted data remain correct.\n",
    "\n",
    "Analogously to the [adversarial AE detector](https://arxiv.org/abs/2002.09364), which is also part of the library, the model distillation detector picks up drift that reduces the performance of the classification model. \n",
    "\n",
    "Moreover, in this example a drift detector that applies two-sample [Kolmogorov-Smirnov](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) (K-S) tests to the scores is employed. The p-values obtained are used to assess the harmfulness of the data. \n",
    "\n",
    "### Dataset\n",
    "\n",
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) consists of 60,000 32 by 32 RGB images equally distributed over 10 classes. We evaluate the drift detector on the CIFAR-10-C dataset ([Hendrycks & Dietterich, 2019](https://arxiv.org/abs/1903.12261)). The instances in\n",
    "CIFAR-10-C have been corrupted and perturbed by various types of noise, blur, brightness etc. at different levels of severity, leading to a gradual decline in the classification model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from alibi_detect.cd import KSDrift\n",
    "from alibi_detect.ad import ModelDistillation\n",
    "\n",
    "from alibi_detect.models.tensorflow import scale_by_instance\n",
    "from alibi_detect.utils.fetching import fetch_tf_model, fetch_detector\n",
    "from alibi_detect.utils.tensorflow import predict_batch\n",
    "from alibi_detect.saving import save_detector\n",
    "from alibi_detect.datasets import fetch_cifar10c, corruption_types_cifar10c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data\n",
    "\n",
    "Original CIFAR-10 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "X_train = X_train.astype('float32') / 255\n",
    "X_train = scale_by_instance(X_train)\n",
    "y_train = y_train.astype('int64').reshape(-1,)\n",
    "X_test = X_test.astype('float32') / 255\n",
    "y_test = y_test.astype('int64').reshape(-1,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For CIFAR-10-C, we can select from the following corruption types at 5 severity levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['brightness', 'contrast', 'defocus_blur', 'elastic_transform', 'fog', 'frost', 'gaussian_blur', 'gaussian_noise', 'glass_blur', 'impulse_noise', 'jpeg_compression', 'motion_blur', 'pixelate', 'saturate', 'shot_noise', 'snow', 'spatter', 'speckle_noise', 'zoom_blur']\n"
     ]
    }
   ],
   "source": [
    "corruptions = corruption_types_cifar10c()\n",
    "print(corruptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pick a subset of the corruptions at corruption level 5. Each corruption type consists of perturbations on all of the original test set images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "corruption = ['gaussian_noise', 'motion_blur', 'brightness', 'pixelate']\n",
    "X_corr, y_corr = fetch_cifar10c(corruption=corruption, severity=5, return_X_y=True)\n",
    "X_corr = X_corr.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the corrupted data by corruption type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c = []\n",
    "n_corr = len(corruption)\n",
    "n_test = X_test.shape[0]\n",
    "for i in range(n_corr):\n",
    "    X_c.append(X_corr[i * n_test:(i + 1) * n_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the same instance for each corruption type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide_input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAAD3CAYAAADmIkO7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANNUlEQVR4nO3dW4xdV33H8d//nDnnzP3mmfgyHhNjagtCaBQeaCtwI0QFqkjhJZWq9DGtVMETAqSWPhQJKJUKgqpSW0VVi7iIvrRAWySgagMRVdMmqoKCIdQmCZOLL2M7Y4/nfs7iwafVyKy1J/vfGc+/6fcjjeTZa/Z/1jlzfmdZe5+1lqWUBCCexn53AEAe4QSCIpxAUIQTCIpwAkERTiAowhmImb3NzJ7ehTqPmNlDu9En7B/CGUhK6dGU0qnb+TvN7Fkze8ft/J14ZQgnEBTh3Af90ep3zeyMmV01s78ys0Ezu8/Mnu//zAkzu2Jm9/a/P2Jml8zsvv73v2Bm/2pmL5vZk/99PPO7TpjZP5vZZTNbNLMvmtlkv+3zko5J+nszWzazD9epjT2WUuLrNn9JelbSU5LmJU1L+q6kj0m6T9Lz237utySdkTQs6RuS/rh/fE7SZUm/qptvsL/S/3623/6IpIf6/35dv70jaVbSdyR95pa+vGPb95W1+bp9X4yc++dPU0oLKaUrkj4u6Tdu/YGU0sOSzkp6TNJhSR/pN/2mpK+nlL6eUuqllL4l6XHdDNStNc6mlL6VUlpPKV2S9GlJv1zRr1dcG3trYL878P/YwrZ/PyfpSOHnHpb0NUm/nVJa7x97jaQHzOz+bT/XkvQvt55sZgclfVbS2ySN6eZoeLWiX6+4NvYWI+f+md/272OSXrz1B8xsVNJnJP2lpD8ws+l+04Kkz6eUJrd9jaSUPpn5PZ+QlCTdnVIa182R0ba13zotqU5t7CHCuX/eZ2ZH+4H7iKS/yfzMZyU9nlJ6SNI/Svrz/vEvSLrfzN5pZs1tF5OOZmqMSVqWtGRmc5I+dEv7BUmv3fZ9ndrYQ4Rz/3xJ0jcl/VjSOd28IPQ/zOw9kt4l6Xf6hz4g6V4zezCltCDpPZJ+T9Il3RztPqT83/Ojku6VtKSbAf/bW9r/UNLv96/MfrBmbewh61+hw21kZs/q5tXUf9rvviAu3g2BoAgnEBT/rQWCYuQEgqr8EMLi4qJrWH21jsZmtvMP7bdez3VaauQfW5LzMffK5zUavj567MVLcbdfBzMzM9mCjJxAUIQTCIpwAkERTiAowgkERTiBoAgnEBThBIIinEBQhBMIinACQRFOICjCCQRVOSvF++n7V+uslNv5uLzPvZl3xkcze7TrLdeoeN93PI/2M4sEvvoxcgJBEU4gKMIJBEU4gaAIJxDUnlytjb7Wjveq6148rt2+Atxr5K+67mRlrZs9/pPnz7vqDY50im3TIyO1602O1T9HktIeXOW9Xa9vRk4gKMIJBEU4gaAIJxAU4QSCIpxAUIQTCIpwAkERTiAowgkERTiBoAgnEBThBIKqnpXiLHo756Qkxy8zZw8bFbMRvHMfur3dnTXx3PlF13kvX1/PHl+6tuyqN761WWybGBmtX9D5oqo+LfasK0ZOICjCCQRFOIGgCCcQFOEEgiKcQFCVt1KWVtZcRQdb7UKLb23/Vqu8aFXTc1W757yEXtG2vLrqqrnZyy+sNdBqueotLq24zlvdzPfjRuH4Ti4vXCi2ra3lb9tU+aU33+XqhwrPr/S/uCVSOK+xy2uJMXICQRFOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiKcAJBEU4gKMIJBEU4gaAqP/j+7z98xlW03cyXnZgcctU7eexIsW3IszN08r0n9RrlD0pvbG24ajYKXem0yjtDVzk5d9B1Xnsov3P02pbv09xdKz/Haxv1Jwmce+GSqx+pt1Vsmzkw6ao5NTpc+GWuckWMnEBQhBMIinACQRFOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiKcAJBEU4gKMIJBFU5K+Xbj37XVbS0LP3s9Lir3p2//t5yY6/+bJBO2zc7Zqti9f6hsTFXTStszd1zbhUwOu6bzdIrvE+vXF9y1fv+uXPFtosXyls1lNx42bfD9sry9WLb6dNvcdV885tenz3u22ykjJETCIpwAkERTiAowgkERTiBoAgnEJSligWyvv2f51xLFg218ndoRjqDnnLqtMq7E1++uFC7Xqvju5Wy1i1fLH/qRz921WwO5HcBHx329XF6asJ13tH5o9njXc8CapKur5QX8Wo47jn01nw3KhYWniu23fXGU66a4+P5v81Q27cb+cz4ePa+GSMnEBThBIIinEBQhBMIinACQRFOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiKcAJBVS7w9ZWvfdNV1Lr5GQkHxqZc9U6+8USxbXZ6tHa90alZVz+e/I8ni21/8dd/56qZCquhjbSbrnrvPP1W13kn5o9lj4+N+mYSTbbKC40t36i/WNdaqr9VvSTd9Ybya+fCSxddNc+fzx8/On/EVW9mPL/wHSMnEBThBIIinEBQhBMIinACQVVerX3ssSdcRacm82usnHqDb4uBx5/4t2Lbqbn6V8gGOvl1e3YyMTFSbHvwgXe7ag4V1lU6cfxOV7122nKd9/SZH2aPX7h42VXvufMvFdtWbtyoXS85NzsYrdgmY2hw2FVzeCJf0wZ9r6tT84eyxxk5gaAIJxAU4QSCIpxAUIQTCIpwAkERTiAowgkERTiBoAgnEBThBIIinEBQhBMIqnJn67n5e1zbGn/qT/4oe/zw3B2ecnrm7NPFtvnpA7XrHX7NcVc/xqYmi21dK+++XWVgMD8r5crL11z1nvqBd4ftfD8aTd9Mi0bF+36vV/9ltbW14eqHVQw/vd6mq+bK9fysmrX1FVe99z/4Xna2Bv4vIZxAUIQTCIpwAkERTiAowgkERTiBoAgnEBThBIIinEBQhBMIinACQRFOIKjKvVLe/q5fcxU9eOxk9vj6um8WwPE3nS62FTaGrrS46ppso0vr68W2tXXfLJKtXn7/mIWXCtsn72Td99g2u/n+P/v8C65603eUdw+/fq3+c/XCwoKrH6by3jGdjm/38NnRiezxwSHfDJ4SRk4gKMIJBEU4gaAIJxAU4QSCqrxaO3XwoKvoV776SPZ4z/te0ChfBTO1apdrtXxX6ZoD5fNabd+VupTyNVeWr7rqWcO3llG3mz/v+tKqq975F8trGa2v19/Zulfo307anfJrrrnl2y173fLPyeCgb+f2EkZOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiKcAJBEU4gKMIJBEU4gaAIJxAU4QSCqpyV8vCffcpV1Hr5zA8N59de2UkzVcz4aFY+hHw/Rn0zSEZHRoptg2MzrprDI4fy9Zr1Z9tI0sCwb2bEZmF9pNUb9WeQSNJmxa7RDau/zlGj7Xs+JsY7xbah6SFXzXYj//puyTfbqYSREwiKcAJBEU4gKMIJBEU4gaAIJxBU5X2ImYn6tykk6erVK9njh+YOu+oNbJYvvZ+/eKF2vfPPXHL1Y3OzfHug1fHdJjp85z3Z45dX11z15Lyc31D+Fkyn5buFMTJWvl118MB07Xp3zx9x9WN2fLDYtrl13VWz08q/DqanDrjqlTByAkERTiAowgkERTiBoAgnEBThBIIinEBQhBMIinACQRFOICjCCQRFOIGgKj/Zvn5t0VV0dTW/7swLZ8+46h2fKH+geDzV33n5mm24+mHKr7MjSd2N8ofiq5z++fxkgPvv+UVXvSuXfX+zXqH/3d6Wq163W36ubqyv1K730tJ/ufqx2hgrto0fusNV88Dh/HpRh+bnXPVKGDmBoAgnEBThBIIinEBQhBMIinACQRFOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiqclbK3Gt9S+AvPp6ffbK8cdVV7+xSedn8ptV/f9ns+WalKFXsyGw9V8lH/+HL2eMPnDnnqvd6822hca2Zf2xnnFsWfG9tudjWPTxVu97xt9zr6sfAcHk38qHxcluVidn8rJRmu7z1gwcjJxAU4QSCIpxAUIQTCIpwAkERTiCoyuvus4d8O1E3mz/IHk+9ilsRFVbVLbZZ1e2NAt9Njx3kN4be0Y2V/GJXL16+6Kr3I8fiWZL0RDv/HG/OH3LVm7z7dcW26YP1X1cDgx1XP9Qsjz8Dbd+u3Y1GvmZzwHcbq/h7drUagF1DOIGgCCcQFOEEgiKcQFCEEwiKcAJBEU4gKMIJBEU4gaAIJxAU4QSCIpxAUJUfo9/q+bZSnzgwlD2+urzmqrdZMeWj65jp0vA9LFVNPelZ01XxamGOzOdWLrjq9e4Yd513+OTx7PG75o+56o3MzBbbrqn+38w6vhkkYxMTxbahkWFXzVa7nT3ebPpeAyWMnEBQhBMIinACQRFOICjCCQRVebV2wLm6/NHjB7LHN1a2XPU2Nsur/nRT/cV7msn3ntSo2PrBWr4rdYPt/HkT0/W3LJCknzs07zpvcjJ/5bI96nsRrHbLl8S7jrV2vOv9WMUV1N1eQ2i3MXICQRFOICjCCQRFOIGgCCcQFOEEgiKcQFCEEwiKcAJBEU4gKMIJBEU4gaAIJxCUJcfO0AD2HiMnEBThBIIinEBQhBMIinACQRFOIKifAvCg83TBXtKoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "\n",
    "n_test = X_test.shape[0]\n",
    "plt.title('Original')\n",
    "plt.axis('off')\n",
    "plt.imshow(X_test[i])\n",
    "plt.show()\n",
    "for _ in range(len(corruption)):\n",
    "    plt.title(corruption[_])\n",
    "    plt.axis('off')\n",
    "    plt.imshow(X_corr[n_test * _+ i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also verify that the performance of a classification model on CIFAR-10 drops significantly on this perturbed dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy:\n",
      "Original 0.9278\n",
      "gaussian_noise 0.2208\n",
      "motion_blur 0.6339\n",
      "brightness 0.8913\n",
      "pixelate 0.3666\n"
     ]
    }
   ],
   "source": [
    "dataset = 'cifar10'\n",
    "model = 'resnet32'\n",
    "clf = fetch_tf_model(dataset, model)\n",
    "acc = clf.evaluate(scale_by_instance(X_test), y_test, batch_size=128, verbose=0)[1]\n",
    "print('Test set accuracy:')\n",
    "print('Original {:.4f}'.format(acc))\n",
    "clf_accuracy = {'original': acc}\n",
    "for _ in range(len(corruption)):\n",
    "    acc = clf.evaluate(scale_by_instance(X_c[_]), y_test, batch_size=128, verbose=0)[1]\n",
    "    clf_accuracy[corruption[_]] = acc\n",
    "    print('{} {:.4f}'.format(corruption[_], acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model distillation as a malicious drift detector\n",
    "\n",
    "Analogously to the [adversarial AE detector](https://arxiv.org/abs/2002.09364), which uses an autoencoder to reproduce the output distribution of a classifier and produce adversarial scores, the model distillation detector achieves the same goal by using a simple classifier in place of the autoencoder. This approach is more flexible since it bypasses the instance's generation step, and it can be applied in a straightforward way to a variety of data sets such as text or time series.\n",
    "\n",
    "We can use the adversarial scores produced by the Model Distillation detector in the context of drift detection. The score function of the detector becomes the preprocessing function for the drift detector. The K-S test is then a simple univariate test between the adversarial scores of the reference batch and the test data. Higher adversarial scores indicate more harmful drift. Importantly, a harmfulness detector flags **malicious data drift**. We can fetch the pretrained model distillation detector from a [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect/ad/cifar10/resnet32/model_distillation/) or train one from scratch:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition and training of the distilled model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Conv2D, Dense, Flatten, InputLayer\n",
    "from tensorflow.keras.regularizers import l1\n",
    "\n",
    "def distilled_model_cifar10(clf, nb_conv_layers=3, nb_filters1=256, nb_dense=40,\n",
    "                            kernel1=4, kernel2=4, kernel3=4, ae_arch=False):\n",
    "    print('Define distilled model')\n",
    "    nb_filters1 = int(nb_filters1)\n",
    "    nb_filters2 = int(nb_filters1 / 2)\n",
    "    nb_filters3 = int(nb_filters1 / 4)\n",
    "    layers = [InputLayer(input_shape=(32, 32, 3)),\n",
    "              Conv2D(nb_filters1, kernel1, strides=2, padding='same')]\n",
    "    if nb_conv_layers > 1:\n",
    "        layers.append(Conv2D(nb_filters2, kernel2, strides=2, padding='same',\n",
    "                             activation=tf.nn.relu, kernel_regularizer=l1(1e-5)))\n",
    "    if nb_conv_layers > 2:\n",
    "        layers.append(Conv2D(nb_filters3, kernel3, strides=2, padding='same',\n",
    "                             activation=tf.nn.relu, kernel_regularizer=l1(1e-5)))\n",
    "    layers.append(Flatten())\n",
    "    layers.append(Dense(nb_dense))\n",
    "    layers.append(Dense(clf.output_shape[1], activation='softmax'))\n",
    "    distilled_model = tf.keras.Sequential(layers)\n",
    "    return distilled_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n",
    "    return (y_true == y_pred).astype(int).sum() / y_true.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = 'my_path' # change to (absolute) directory where model is downloaded\n",
    "detector_type = 'adversarial'\n",
    "detector_name = 'model_distillation'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:\n",
    "    ad = fetch_detector(filepath, detector_type, dataset, detector_name, model=model)\n",
    "else:\n",
    "    distilled_model = distilled_model_cifar10(clf)\n",
    "    print(distilled_model.summary())\n",
    "    ad = ModelDistillation(distilled_model=distilled_model, model=clf)\n",
    "    ad.fit(X_train, epochs=50, batch_size=128, verbose=True)\n",
    "    save_detector(ad, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scores and p-values calculation\n",
    "\n",
    "Here we initialize the K-S drift detector using the harmfulness scores as a preprocessing function. The KS test is performed on these scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "nb_batches = 100\n",
    "severities = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_batch(x_orig, x_corr, batch_size, p):\n",
    "    nb_orig = int(batch_size * (1 - p))\n",
    "    nb_corr = batch_size - nb_orig\n",
    "    perc = np.round(nb_corr / batch_size, 2)\n",
    "    \n",
    "    idx_orig = np.random.choice(range(x_orig.shape[0]), nb_orig)\n",
    "    x_sample_orig = x_orig[idx_orig]    \n",
    "    \n",
    "    idx_corr = np.random.choice(range(x_corr.shape[0]), nb_corr)\n",
    "    x_sample_corr = x_corr[idx_corr]\n",
    "    \n",
    "    x_batch = np.concatenate([x_sample_orig, x_sample_corr])\n",
    "    return x_batch, perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialise the drift detector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "np.random.seed(0)\n",
    "n_ref = 1000\n",
    "idx_ref = np.random.choice(range(X_test.shape[0]), n_ref)\n",
    "X_test = scale_by_instance(X_test)\n",
    "X_ref = X_test[idx_ref]\n",
    "labels = ['No!', 'Yes!']\n",
    "\n",
    "# adversarial score fn = preprocess step\n",
    "preprocess_fn = partial(ad.score, batch_size=128)\n",
    "\n",
    "# initialize the drift detector\n",
    "cd = KSDrift(X_ref, p_val=.05, preprocess_fn=preprocess_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate scores. We split the corrupted data into harmful and harmless data and visualize the harmfulness scores for various values of corruption severity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading corrupted data. Severity = 1\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Compute p-values\n",
      "Loading corrupted data. Severity = 2\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Compute p-values\n",
      "Loading corrupted data. Severity = 3\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Compute p-values\n",
      "Loading corrupted data. Severity = 4\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Compute p-values\n",
      "Loading corrupted data. Severity = 5\n",
      "Preprocess data...\n",
      "Make predictions on corrupted dataset...\n",
      "Compute adversarial scores on corrupted dataset...\n",
      "Compute p-values\n"
     ]
    }
   ],
   "source": [
    "dfs = {}\n",
    "score_drift = {\n",
    "    1: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    2: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    3: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    4: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "    5: {'all': [], 'harm': [], 'noharm': [], 'acc': 0},\n",
    "}\n",
    "y_pred = predict_batch(X_test, clf, batch_size=256).argmax(axis=1)\n",
    "score_x = ad.score(X_test, batch_size=256)\n",
    "\n",
    "for s in severities:\n",
    "    print('Loading corrupted data. Severity = {}'.format(s))\n",
    "    X_corr, y_corr = fetch_cifar10c(corruption=corruptions, severity=s, return_X_y=True)\n",
    "    print('Preprocess data...')\n",
    "    X_corr = X_corr.astype('float32') / 255\n",
    "    X_corr = scale_by_instance(X_corr)\n",
    "    \n",
    "    print('Make predictions on corrupted dataset...')\n",
    "    y_pred_corr = predict_batch(X_corr, clf, batch_size=1000).argmax(axis=1)\n",
    "    \n",
    "    print('Compute adversarial scores on corrupted dataset...')\n",
    "    score_corr = ad.score(X_corr, batch_size=256)\n",
    "    \n",
    "    labels_corr = np.zeros(score_corr.shape[0])\n",
    "    repeat = y_corr.shape[0] // y_test.shape[0]\n",
    "    y_pred_repeat = np.tile(y_pred, (repeat,))\n",
    "    \n",
    "    # malicious/harmful corruption: original prediction correct but\n",
    "    # prediction on corrupted data incorrect\n",
    "    idx_orig_right = np.where(y_pred_repeat == y_corr)[0]\n",
    "    idx_corr_wrong = np.where(y_pred_corr != y_corr)[0]\n",
    "    idx_harmful = np.intersect1d(idx_orig_right, idx_corr_wrong)\n",
    "    \n",
    "    # harmless corruption: original prediction correct and prediction\n",
    "    # on corrupted data correct\n",
    "    labels_corr[idx_harmful] = 1\n",
    "    labels = np.concatenate([np.zeros(X_test.shape[0]), labels_corr]).astype(int)\n",
    "    idx_corr_right = np.where(y_pred_corr == y_corr)[0]\n",
    "    idx_harmless = np.intersect1d(idx_orig_right, idx_corr_right)\n",
    "    \n",
    "    # Split corrupted inputs in harmful and harmless\n",
    "    X_corr_harm = X_corr[idx_harmful]\n",
    "    X_corr_noharm = X_corr[idx_harmless]\n",
    "\n",
    "    # Store adversarial scores for harmful and harmless data\n",
    "    score_drift[s]['all'] = score_corr\n",
    "    score_drift[s]['harm'] = score_corr[idx_harmful]\n",
    "    score_drift[s]['noharm'] = score_corr[idx_harmless]\n",
    "    score_drift[s]['acc'] = accuracy(y_corr, y_pred_corr)\n",
    "    \n",
    "    print('Compute p-values')\n",
    "    for j in range(nb_batches):\n",
    "        ps = []\n",
    "        pvs_harm = []\n",
    "        pvs_noharm = []\n",
    "        for p in np.arange(0, 1, 0.1):\n",
    "            # Sampling a batch of size `batch_size` where a fraction p of the data\n",
    "            # is corrupted harmful data and a fraction 1 - p is non-corrupted data\n",
    "            X_batch_harm, _ = sample_batch(X_test, X_corr_harm, batch_size, p)\n",
    "            \n",
    "            # Sampling a batch of size `batch_size` where a fraction p of the data\n",
    "            # is corrupted harmless data and a fraction 1 - p is non-corrupted data\n",
    "            X_batch_noharm, perc = sample_batch(X_test, X_corr_noharm, batch_size, p)\n",
    "            \n",
    "            # Calculating p-values for the harmful and harmless data by applying\n",
    "            # K-S test on the adversarial scores\n",
    "            pv_harm = cd.score(X_batch_harm)\n",
    "            pv_noharm = cd.score(X_batch_noharm)\n",
    "            ps.append(perc * 100)\n",
    "            pvs_harm.append(pv_harm[0])\n",
    "            pvs_noharm.append(pv_noharm[0])\n",
    "        if j == 0:\n",
    "            df = pd.DataFrame({'p': ps})\n",
    "        df['pvalue_harm_{}'.format(j)] = pvs_harm\n",
    "        df['pvalue_noharm_{}'.format(j)] = pvs_noharm \n",
    "\n",
    "    for name in ['pvalue_harm', 'pvalue_noharm']:\n",
    "        df[name + '_mean'] = df[[col for col in df.columns if name in col]].mean(axis=1)\n",
    "        df[name + '_std'] = df[[col for col in df.columns if name in col]].std(axis=1)\n",
    "        df[name + '_max'] = df[[col for col in df.columns if name in col]].max(axis=1)\n",
    "        df[name + '_min'] = df[[col for col in df.columns if name in col]].min(axis=1)\n",
    "    df.set_index('p', inplace=True)\n",
    "    dfs[s] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now plot the mean scores and standard deviations per severity level. The plot shows the mean harmfulness scores (lhs) and ResNet-32 accuracies (rhs) for increasing data corruption severity levels. Level 0 corresponds to the original test set. Harmful scores  are scores from instances which have been flipped from the correct to an incorrect prediction because of the corruption. Not harmful means that a correct prediction was unchanged after the corruption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_noharm, std_noharm = [], []\n",
    "mu_harm, std_harm = [], []\n",
    "acc = [clf_accuracy['original']]\n",
    "for k, v in score_drift.items():\n",
    "    mu_noharm.append(v['noharm'].mean())\n",
    "    std_noharm.append(v['noharm'].std())\n",
    "    mu_harm.append(v['harm'].mean())\n",
    "    std_harm.append(v['harm'].std())\n",
    "    acc.append(v['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KjupvEho0IG3qFOKqVGF1j57s/uQTv0NSZUiFChWYMWMGW7duLbpnz6xZs1i2bFnEYsrOzg6rv40bN7Jo0SK+//77ApNVJDVo0KDUp1kSCtp3rV+/nsWLFx/5rF+/nsmTJ/PAAw+wZMmSApNVeeVrwhKRB3GrNS+w9l5jzFhjTEtjTMv4+Ogp1JiYmkra1ClUOPlkMvsPYPvkkqqAWMW6+Ph4+vbty6hRo/7QbdWqVbRu3ZrTTjuNNm3asGbNGr766itmz57NkCFDaNGiBcuXLy/W9Nq2bYtt2zRt2pS///3vrF+/HnDPGgYPHszpp5/O/PnzqVSpEkOGDOGUU07h0ksvZeHChbRq1YqmTZsye7ZbEfLll1/OunXraNGiBV9++SWtWrVi8eLFAGzdupW0tLTjWzhl0HvvvcfZZ5/NGWecwaWXXsqmTZsA98y3W7dunH/++XTr1o2MjAx69OjBhRdeSGpqKjNmzGD06NF06dKFgQMHcvrppzNnzhymTZvGP/7xD7p27cq8efO4+uqrj0yrX79+TJw40ac59Z9vCUtEegJXA11NGa3jJL5GDVInvkqliy9m078eYfNTT2FycvwOS5UBd955J5MnT2bXrqMvIfbv358ePXrw/fff07VrVwYMGMB5551H+/btefLJJ1myZAknnHDCH8Y3atQoWrRoceSTm5QA3nzzTWbNmsXMmTOZPXs2FSpUAGDv3r2cffbZfPfdd1xwwQXs3buX1q1bs3TpUipXrsxDDz3E3LlzmTlzJkOHDgVg9uzZnHDCCSxZsoQLL7wwgksouuzfv/+o5Zu7PAAuuOACFixYwLfffkunTp144oknjnRbtmwZH3/8MVOmTAFg+fLlfPrpp8yePZubb76ZSy65hPfee4+kpCQ++OADbr311iPrerIeBP+BL6csItIWuBe42Bizz48YSkpcxYo0eu5ZNj7yCNvGjefwho3Uf+xR4hIT/Q5NRbEqVarQvXt3nn322aMu+8yfP58ZM2YA0K1bN+69996wxjdw4MA/3MPK9eyzzzJ16lQANmzYwC+//ELNmjUJBAJcf/31R/pLTEykbVu3fNSpp55KhQoVSEhI4NRTT2XVqlXHOqsxITk5mSVLlhxpzr2HBe6zfTfddBMbNmzg0KFDRz0D1b59+6PW75VXXnlkmWZnZ9O2bVt+/vlnTjrppHK/jMNRGsXapwDzgZNFJFNEegPPA5WBuSKyRETGRDqOSJL4eOo9/DC1Bw3it/ffZ22fvmT/9pvfYakod/fdd/PKK69EtAaCefPm8fHHHzN16lTeffddzjjjjCPP6SQlJREIBI70m5CQcKRYdFxc3JEzsbi4uALvwcTHx5PjXVWI5ed/CtO/f3/69evHDz/8wMsvv3zUckhJSTmq39BlGrq8RSTfZRy6fKH8LuNcEU9YxpjOxpj6xpgEY0wjY8wrxpgTjTGNjTEtvM9tkY4j0kSEWn370OCJx9n3zTes7nozhzds8DssFcVq1KjBjTfeyCuvvHKk3XnnnXfkbGjy5MlHLrtVrlyZ3bt3F3sau3btonr16iQnJ7NixQoWLFhQMsF70tLS+PrrrwGYPn16iY67rNi1axcNGzYEYNKkSSU67tTUVJYtW8bBgwfZuXMnn5TzAl7RU4ohRlRt35742rXJ7D+AVZ0603jsWJJOPsnvsFRBCiiG/tNPPwFw8sknR3TygwcPPqpo9HPPPUevXr148sknqV27Nq+++ioAnTp1ok+fPjz77LNMnz493/tY+Wnbti1jxoyhXbt2pKenc84555Ro/Pfccw833ngjY8eO5aqrrirRcRdk+jluYoz0uglXRkYGN9xwA9WrV6d169asLMHnMxs3bsyNN97In/70J9LT0znjjDNKbNxlkZSl8g4pKSmmrFTgeOCnn1jbpy85+/bR6PnnSCnhHYU6No7jYNt2kf2VVsIqLbE0P7E0L1Dy85Pff1xE9hljUgoYpMyIhuewYlLSySeT9tZUEurXY02fvux6r2QfoFRKqfJGE1YEJdSvT+rkyVQ84wzWDxnC1nHjKEtntEopFU00YUVYoEoVGo8fR5V27djy1NNs+te/MGG+VUBFhh40qFgV6/9tTVilIC4xkQYjn6Tmrb3Z8eYUMgfcRc7+/X6HVaIyMjIQkSOfwt6L56ekpCS2bdsW8xu2Kn9y68NKSkoq9rCOZbd1LPsnx7J/dSz7/ny6pzqW/Ylj2d87lj3PsexGId16OJb9i/fpcZyzUSgtdFHKtr8xmU2PPkryaafRaMxLxFev7ndIJaZVq1aA++xPtAq3xuGNGzcCUK9evdIIK+JiaX5iaV6gZOenoBqHCyt04Vh2ALfWjMuATGAR0NkOOstC+nkbeN8OOpMcy24N9LKDTjfHsmsAi4GWgAG+Bv5sB50dxz0z+dBi7aWsxs1dia9bh/X3DGF1p840HjeWxCZN/A6r3EhISAirNtbbb78diO7kWxyxND+xNC8QFfNzFvCrHXRWADiWPRXoAIS+bbk5MMj7/Rkwy/t9BTDXDjrbvWHn4lYnNSUSgeolQR9Uuewymrz6Ktk7d7KqU2f2/1B09QZKKXUc4nNrvfA+fUO6NQTWhjRneu1CfQdc5/3uCFR2LLtmmMOWGE1YPql45hmkTplCXMWKrO7eg92ffeZ3SEqp2JWVW+uF9xlbzOHvAS52LPtb4GJgHVDqpcc0YfmoQtN0t4qSpk3JvLMfO6a+5XdISqnyZx3QOKS5kdfuCDvorLeDznV20DkDeNBrtzOcYUuSJiyfxdeqReprk0i58AI2ZmSwefRoLcGmlCpNi4BmjmWnO5adCHQCZof24Fh2Lceyc/PFA8AE7/dHwOWOZVd3LLs6cLnXLiI0YUWBuJQUGr/wAtVu+CvbxrzMhvsfwBw65HdYSqlywA46WUA/3ETjANPsoLPUsexhjmW393prBfzkWPbPQF3gUW/Y7cC/cJPeImBYbgGMSNBSglFC4uOpN2wY8fXrs/XZ58jasoWGzz5DoFIlv0NTSsU4O+jMAebkaTc05Pd0IN/X8dtBZwK/n3FFlJ5hRRERofYdd1D/scfYu3Ahq2/uxuFNm/0OSymlooImrChU7bqONH7pJQ6vWcOqTp04+MsvfoeklFK+04QVpSpdeAGpb7yOyTrMqq43s3fhQr9DUkopX2nCimJJzZuTNmUq8bVqsbb3rfw2Z07RAymlVIzShBXlEhs1JO3NySSddhrrBg1m26sTtdi7Uqpc0oRVBgSqVaPJhFeofMUVbH78cTYNH65VlCilyh1NWGVEXIUKNBz1NDV69GDHa6+zbuAgcop447hSSsWSiCcsEZkgIptF5MeQdjVEZK6I/OJ9x04dGxEkcXHUfeB+6tx/H7vnzmXNLb3J2hGRt/grpVTUKY0zrIm4r5sPdT/wiTGmGfCJ16zCVLNnTxqOepoDP/7I6i5dOZSZ6XdISikVcRFPWMaYL4C8r+roAEzyfk8Cro10HLGmStu2NJnwClnbt7tVlPy41O+QlFIqovy6h1XXGLPB+70R991U+RKRvrl1uGRlZZVOdGVExZYtSXtzMnGJiazu3p09X3zhd0hKKRUxvhe6MG4Z7QLLaRtjxubW4RIfr68+zKvCCSeQOnUKiWmprL39DnZOz/d1X0opVeb5lbA2iUh9AO9bX5h3HBLq1CH1tddJOfdcNjz0D7Y897w+q6WUijl+JazZQA/vdw/gXZ/iiBmBSik0fulFqnbsyNYXXmDDQw9hDh/2OyyllCoxEb/GJiJTcOtSqSUimcDDwAhgmoj0BlYDN0Y6jvJAEhKo/9ijJNSvz9YXXyRr8xYajR5FXEqK36EppdRxi3jCMsZ0LqBTm0hPuzwSEWoP6E98/XpszPgnq7t1p/HLY4ivXdvv0JRS6rj4XuhCRUb1G26g8YsvcHDlSlZ16szBFSv8DkkppY6LJqwYVunii0l97TVyDhxgVecu7Pv6a79DUkqpY6blxGNc8ql/Im3qFNbe2oc1vW6hwZNPUuWKy/0OSykVRRzLbgs8AwSA8XbQGZGnexPclzxU8/q53w46cxzLTgMc4Cev1wV20LktUnHqGVY5kNi4MalTp5DUvDnr7r6b7a+95ndISqko4Vh2AHgBuBJoDnR2LLt5nt4eAqbZQecMoBPwYki35XbQaeF9IpasQBNWuRFfvTpNJr5K5UvbsOmx4Wx6/AlMTo7fYSml/HcW8KsddFbYQecQMBX39XmhDFDF+10VWF+K8R2hCasciUtKouHo0VTv2pXtr77KusGDyTl40O+wlFL+agisDWnO9NqFygBudiw7E5gD9A/plu5Y9reOZX/uWPaFkQxUE1Y5I4EAdR96kDpDhrD73x+ytvetZO/a5XdYSqnIis99J6v36VvM4TsDE+2g0whoB7zuWHYcsAFo4l0qHAS86Vh2lULGc1w0YZVDIkLN3rfQYORI9n33Hau6dOXwunV+h6WUipys3Heyep+xId3WAY1Dmht57UL1BqYB2EFnPpAE1LKDzkE76Gzz2n8NLAdOitRMaMIqx6pefRVNxo0ja/NmVv71Bn776D9+h6SUKn2LgGaOZac7lp2IW6hidp5+1uC97MGxbBs3YW1xLLu2V2gDx7KbAs2AiD30qQmrnEs552zS3ppKQoMGrLvrLtbdM0QvESpVjthBJwvoB3yEW0R9mh10ljqWPcyx7PZeb4OBPo5lfwdMAXraQccAFwHfO5a9BJgO3GYHnbz1H5YYfQ5LUaFpU9KmTmHr2LFsfWkM+xYupP4j/6LSRRf5HZpSqhTYQWcObmGK0HZDQ34vA87PZ7h3gHciHqBHz7AU4L44t/add5I2dSqBqlVY2/dvbPjHULL37PU7NKWUAjRhqTyS/3QKadOnU/PW3uycPp2VHTqwd+FCv8NSSilNWOqP4ipUoM4995A6+Q0IBFjToyebho8g58ABv0NTSpVjmrBUgSqeeSZNZ82keudObJ80iZUdr2P/99/7HZZSqpzShKUKFVexIvWGDqXxK+PJ2b+fVZ27sPmZZzCHDvkdmlKqnNGEpcJS6fzzaTr7Xapecw3bXhrDyps6ceCnn/0OSylVjmjCUmELVKlCgxHDafTC897Dxn9l69hxmOxsv0NTSpUDmrBUsVVu04am779H5dat2fL006zu0pWDK1f6HZZSKsZpwlLHJL56dRqOHkWDkSM5uGoVKzteR6u9+xBj/A5NKRWjfE1YIjJQRJaKyI8iMkVEkvyMRxWPiFD16qtoOns2Fc/6Czft3s2AHTv1RbpKqYjwLWGJSENgANDSGPMn3GqXO/kVjzp2CXXr0Pjll3mjSmXSDh9mRfsO7HznHYyebSmlSpDflwTjgWQRiQcq4lMtlur4iQj/rViRR2rWJKl5czY8+BCZt9/B4c2b/Q5NKRUjfEtYxph1wEjc19ZvAHYZY/5Qv4WI9M2tdCwrK6u0w1TFtC0+QJNJE6n79wfYO38+K69pz2///rffYSmlYoCflwSrAx2AdKABkCIiN+ftzxgzNrfSsfh4fbl8WSBxcdTo3p30mTNISE1l3cBBrBs0iKwdO/wOTSlVhvl5SfBSYKUxZosx5jAwAzjPx3hUCavQtClpb06m9t1389vcj1nRvj27P/vM77CUUmWUnwlrDXCOiFQUEcGtzdLxMR4VARIfT63b/kb629OIr16DzNvvYP2DD5K9Z4/foSmlyhg/72H9D7eGym+AH7xYxvoVj4qsJMsibfrb1Ozbl10zZ7GifXv2Lljgd1hKqTLE11KCxpiHjTGWMeZPxphuxpiDfsajIisuMZE6gwaS9uZk4hIrsKZnLzY+8ig5+/f7HZpSqgzwu1i7KoeSW7QgfeYMqnfrxo433mDltR3Z9+23foellIpyWuxO+SIuOZl6D/6dym3asOHvf2d115up2bs3tfr3Iy4x0e/wlCpXHMtuCzyD+wKH8XbQGZGnexNgElDN6+d+O+jM8bo9APQGsoEBdtD5KFJx6hmW8lXKOWeTPvtdql7XkW3jxrHqrzdwwNGyN0qVFseyA8ALwJVAc6CzY9nN8/T2EDDNDjpn4L6R6EVv2OZe8ylAW+BFb3wRoQlL+S5QqRINHnmERmNeImvHdlbeeMzkD0IAACAASURBVBNbx4zB6IPiSpWGs4Bf7aCzwg46h4CpuM/IhjJAFe93VX5/K1EHYKoddA7aQWcl8Ks3vojQhKWiRuVWrWg6ezZVLruMLaOfYVXnLhxcscLvsJSKBfG5bwzyPn1DujUE1oY0Z3rtQmUANzuWnQnMAfoXY9gSowlLRZX46tVp+PRTNBz1NIfXrmVlx+vYPmkSJifH79CUKsuyct8Y5H2K+whRZ2CiHXQaAe2A1x3LLvX8oQlLRaUqV15J0/dmk3LuuWwaPoI1PXpyKDPT77CUikXrgMYhzY28dqF6A9MA7KAzH0gCaoU5bInRhKWiVnzt2jR66UXqP/YYBxyHle07sGPaNK22RKmStQho5lh2umPZibiFKGbn6WcN7tuIcCzbxk1YW7z+OjmWXcGx7HSgGbAwUoFqsXYV1USEatd1JOWcs1n/9wfZOPRhdn/8MfX/9QgJdev4HZ6KgFMnnVpo9xUbV4TV3w89fiixmI5HtM+PHXSyHMvuB3yEW2R9gh10ljqWPQxYbAed2cBgYJxj2QNxC2D0tIOOAZY6lj0NWAZkAXfaQSc7IoGiCUuVEQkNGtBkwivseHMKm0eOZEX79tR76CGqXH0V7qsoy7do3ymq6OY9UzUnT7uhIb+XAecXMOyjwKMRDdCjlwRVmSFxcdS4uStNZ82kQno664cMYd1dd5O1fbvfoSmlSoGeYakyJzEtjdTJb7BtwgS2PvscK77+mvrD/knlNm3CHoeekShV9ugZliqTJBCgVp8+pE2fTnydOmTe2Y/1991P9m+/+R2aUipCNGGpMi3p5JNIf2sqte64nV3vv8+K9h3Y89//+h2WUiofjmVfczzPb2nCUmWeJCZSe8AA0qZOIa5iRdb2vpXMgQM5lBmxx0GUUsfmJuAXx7KfcCzbKu7AmrBUzEg+9VTSZ7xDrTvvZM9n81jRrh2bn3paazdWKkrYQedm4AxgOTDRsez5jmX3dSy7cjjDa6ELFbayUFAhLimJ2v37Ue2Gv7Jl1Ci2jRvHzhkzqH3XAKpdfz0SiNiLpJVSYbCDzm+OZU8HkoG7gY7AEMeyn7WDznOFDatnWComJdSrR4PHHyft7WkkpqaycejDrLzuevbOn+93aEqVW45lt3cseyYwD0gAzrKDzpXA6bgPJxdKz7BUTEs+9VRSJ7/B7o8+YvOTI1nT6xYqXXIJ9ZsZNtTUB46VKmXXA6PsoPNFaEs76OxzLLt3UQPrGZaKeSJClbZtaTrnA2oPHsS+hQt5anw2PeZmk7Jf30uoVCnKIORdg45lJzuWnQZgB51PihpYE5YqN+IqVKBWnz6c8NGHfHaacOXXhufGZHPlohwC2Zq4lCoFbwOhdQVle+3C4mvCEpFqIjJdRIIi4ojIuX7Go8qH+Fq1GHdlgCG3BFhRT+j1cQ4jx2dz5i85oG+CVyqS4r1ajQHwfieGO3DYCUtEkkXk5GIGV5RngA+NMRbuTTenhMevVIHW1hEe6RTHiL+6m8H903N4aGoOjTdr0lIqQrY4lt0+t8Gx7A7A1nAHDqvQhYhcA4zEzYTpItICGGaMaV/4kIWOsypwEdATwBhzCDhU2DBKlTgRvmkmfNdUuPwbww3/l8OTE7KZRXXGsMvv6JSKNbcBkx3Lfh4QYC3QPdyBwz3DygDOAnYCGGOWAOnFCvOP0nErAHtVRL4VkfEikpK3JxHpKyKLRWRxVlbWcU6yZGRkZCAiRz4ZGRl+h6SOU3ZA+Pdf4uh/W4B//1loTyVm0IAO83NIyNIzLqVKgh10lttB5xygOWDbQec8O+j8Gu7w4Sasw8aYvIebx7sVxwNnAi8ZY84A9gL35+3JGDPWGNPSGNMyPj46SuFnZGRw8cUXc/HFF2OM0YQVQ/YmC5MuC3ATG/iGA3Sdl8Oosdmc6+j9LfU7PWg9do5lXwXcAQxyLHuoY9lDixomV7gZYKmIdAECItIMGAB8VfxQj5IJZBpj/uc1TyefhKWUH1aTxSC20r5zGt0/yWHgrByubAST2gRY3kCf3yrvMjIymDdvHsCRb1U0x7LHABWBS4DxwF8JKeZelHDPsPoDpwAHgTeBXbiv1DhmxpiNwNqQghxtcKtZVipq/JgWx329Arx8ZRz1tsPwSdn0m51Njd/0bEupY3CeHXS6AzvsoPNP4FzgpHAHLvIMS0QCwAfGmEuAB485zPz1ByaLSCKwAuhVwuNX6riZOOGTFsJXtnDt/ByuWmg4+6dsZp8t5Nywj7iKFf0OUanj4lh2W9xS2wFgvB10RuTpPgr3rAjcM6Q6dtCp5nXLBnJfELrGDjqFFcY74H3vcyy7AbANqB9unEWeYRljsoEcr1RfiTLGLPHuT51mjLnWGLOjpKehVEnZX0GY0irAwL4Bvm4m3PBfw/K2V7Jz5ixMTk7RIyjH9J5P9HIsOwC8AFyJWxiis2PZzUP7sYPOQDvotLCDTgvgOWBGSOf9ud2KSFYA7zmWXQ14EvgGWIV71S4s4d7D2gP8ICJzcQtHAGCMGRDuhJSKFVuqCaOvDTCnpWHkd/XY8MAD7HjjDerefx8V//IXv8OLSnrPJ6qdBfxqB50VAI5lTwU6UPAtms7Aw8WdiFdx4yd20NkJvONY9vtAkh10wn5+JNx7WDOAfwBfAF+HfFQZF0tHvqU9Lz83EtKmTqHBk0+QtW0bq7t1J7P/AA6tWVMi44+ldaN8F5/7eJD36RvSrSHu81C5Mr12f+BYdiruI0mfhrROcix7sWPZCxzLvragAOygk4N7JpfbfLA4yQrCPMMyxkzy7jPl3hz7yRhzuDgTUtEplo58/ZgXiYuj6jXXUPnSS9n26qtsGzeePfPmUb1bN2rdfhuBymHVS5evWFo3yndZxpiWJTCeTsB0O+hkh7RLtYPOOseymwKfOpb9gx10lhcw/CeOZV8PzLCDTrFLLoV1hiUirYBfcLPji8DPInJRcSemVKyKS06m9h13cMKHH1Ll6qvZ/uqrLL/8CnZMmYKJkgfelSrAOqBxSHMjr11+OgFTQlvYQWed970Ct56rMwqZ1t9wX3Z70LHs3xzL3u1Y9m/hBhruPayngMuNMT8BiMhJXtB/DndCSpUHCXXr0GD4Y1S/uSubh49g4z+HsePNN6lz731UuvACv8NTKj+LgGaOZafjJqpOQJe8PTmWbQHVgfkh7aoD++ygc9Cx7FrA+cATBU3IDjrHfsmB8BNWQm6yAjDG/CwiCcczYaViWfIpp9Dk9dfYPXcum58cydo+fUi56ELq3ncfFU44we/wlDrCDjpZjmX3Az7CLdY+wQ46Sx3LHgYstoPObK/XTsDUPJfybOBlx7JzcK/YjbCDToHP0zqWne+VubwVOhYk3IS1WETGA294zV2BxWEOq1S5JCJUufxyKrVqxY7X32DrSy+xon0Hqt90E7X69yO+enW/Q1QKADvozAHm5Gk3NE9zRj7DfQWcWoxJDQn5nYRbQvFroHU4A4ebsG4H7sR9JRPAl7j3spRSRYhLTKRm71uo2vFatjz3HDumTmXX++9T6/bbqdG1C5IYdnVASpVpdtC5JrTZsezGwOhwhw+3WHs88Iwx5jpjzHXAs7injkqpMMXXqEH9hx+m6buzSD7tNDY//jjLr7mG3R9/jNEX66ryKRP3smJYwj3D+gS4FPcBYoBk4D/AecUKTSlFhWbNaDJ+HHu++IJNjz9BZr/+VDzrLOo+cD9JdtjbrlJljmPZz/F7TR9xQAvcN16EJdyElWSMyU1WGGP2iIi+QE2p41DpootIOfdcdkybxtbnnmfldddT9bqO1L7rLhLq1PE7PKUiIbTsQxYwxQ46/w134HAvCe4VkTNzG0SkJbA/3IkopfInCQnU6NqVEz76kBo9erBr9nssb3slW8eMIefAgaJHoFTZMh14ww46k+ygMxlY4Fh22Cc/4Sasu4G3ReRLEfkSmAr0K36sSqn8BKpWpe7993HCe7NJOe9ctox+huXt2tFy/36tOFLFkk9wbynlSgY+DnfgQhOWiPxFROoZYxYBFvAWcBj4EFhZ/FiVUoVJTEuj8fPP02TiRAJVq9F712/037GTrK1b/Q5NqZKQZAedI7eXvN8ldob1MnDI+30u8Hfc1zPtAMYWL06lVLhSzjmb9OlvM6VKZU48dIgVHTuyd8ECv8NS6njtdSz7yO0lx7L/TDFuLxVV6CJgjNnu/b4JGGuMeQd4R0SWFDtUpVTYJBDgi4oV+TUhgX9VrsKaXrdQ6/bbqXXnHUhAnypRZdLdwNuOZa8HBKiHm1vCUtQZVkBEcpNaG45+pXy4JQyVUsdhfUIC6dPfpmqHDmx98UXW9OzF4U2b/A5LqWKzg07u7aXbgdsA2w46YVdVVVTCmgJ8LiLv4p62fQkgIicCxarHRCl17OIqVqTBiOHUHz6c/T/+yMprO7Lni7Bev6ZU1HAs+04gxQ46P9pB50egkmPZd4Q7fKEJyxjzKDAYmAhcYH5/HD8O6H9sISuljlW1jteS/s504mvXZm3fv7F55EjMYa2aTpUZfbwahwGwg84OoE+4AxdZrN0Ys8AYM9MYszek3c/GmLCfTlZKlZwKTZuSNu0tqt10E9vGv8Lqbt2ptUuLvqsyIeBYtuQ2OJYdAMJ+mWa4z2FFjIgERORbEXnf71iUKivikpKo/88MGj79FAd/+YUnJmTT8uccv8NSqigfAm85lt3Gsew2uLed/h3uwL4nLOAuwPE7CKXKoirt2pE+4x02VYN738mhx9xs4rP0bEtFrftwC+/d5n1+4OgHiQvla8ISkUbAVcB4P+NQqixLTE3lH90CfNBSuGqx4ZHXs6m7XZOWij520MkB/geswq0LqzXFOGHxu2j6aOBeoMBqk0WkL9AXIFHrDVIqX1nxwqTLAixLzeH2D3J4/NVsXm4Xx3w7Gi6iqPLOseyTgM7eZyvuW5Owg84lxRmPb/9mEbka2GyMKbQMvjFmrDGmpTGmZXy83/lVqei26KQ47r0lwNraMHBWDn0+zCbhsJ5tKd8Fcc+mrraDzgV20HkOyC7uSPzMAOcD7UWkHW5VyVVE5A1jzM0+xqRUmbe1qpDRNUCnL3LosMBwUmY2g4hnNVl+h6ailGPZbYFncCvmHW8HnRF5uo8Ccs+GKgJ17KBTzevWA3jI6/aIHXQm5TOJ64BOwGeOZX+I+wJ1yae/Qvl2hmWMecAY08gYk4Y7I59qslKqZGQHhMmXBHjsxjiq74E3qMdVpPgdlopCXtHyF4ArgeZAZ8eym4f2YwedgXbQaWEHnRbAc8AMb9gawMPA2bj3pB52LLt63mnYQWeWHXQ64b7l4jPcVzTVcSz7JceyLw83Vr3ArVQMW3JCHEN6B1jGIf5JTe54P5sKh/QSoTrKWcCvdtBZYQedQ7hnPx0K6b8zbnF0gCuAuXbQ2e49BDwXaFvQgHbQ2WsHnTftoHMN0Aj4FrfkYFii4qaQMWYeMM/nMJSKSTsqC3ewmd5U5dYfqnLi+mxGXRtgbZ1iX5FRZVe8iITW9jvWGJNb40ZDYG1It0zcM6Y/cCw7FUjn9/fK5jdsw3AC8hLcWIpR84eeYSlVDmQDY9nFvzrHkXIAhk/Kps2SHK0csvzIyi285n2OtXqoTsB0O+gUu8BESdCEpVQ5sjQtjnt7B3AaCX/7dw53vZtD8kFNWuXcOqBxSHMjr11+OvH75cDiDnvcouKSoFKq9OxKER7rFEeH+YabvsjhhA3ZjOoYYGU9vURYTi0CmjmWnY6bbDoBXfL25Fi2BVQH5oe0/gh4LKSgxeXAA5EKVM+wlCqHjAizzosjo2uA+Gx45LVs2i7OweglwnLHDjpZQD/c5OMA0+ygs9Sx7GGOZbcP6bUTMNUOOiZk2O3Av3CT3iJgmNcuIvQMS6ly7KfGwr23BLjzgxxumZtDZv/+NHj0UQJVq/odmipFdtCZA8zJ025onuaMAoadAEyIWHAh9AxLqXJuT0Xh8b/GMalNHHvmfc7Kjtexf8kSv8NS6g80YSmlQIQPzooj7c3JIMKqm7ux7ZVXMDlaZYmKHpqwlFJHJJ92GukzZ1C5dWs2PzmStbffTtaOHX6HpRSgCUsplUegShUaPjOaukP/wb6v5rOyw7XsW7TI77CU0oSllPojEaFGly6kTXuLuORkVvfoydaXXsJk+/K8qFKAJiylVCGSbJu0d96hSrt2bHnmWdbceitZW7b4HZYqpzRhKaUKFaiUQoMnn6D+o4+w/9slrLi2I3u/+srvsFQ5pAlLKVUkEaHa9deT/vY0AtWrsab3rWwePRqTpXVsqdKjCUspFbYKzZqR/vbbVL2uI9vGvMzqHj05vHGj32GpckITllKqWOKSk2nw6KM0eOJxDjgOK6/tyO558/wOS5UDmrCUUsekavv2pL8znfh69ci87XY2Pf4E5tAhv8NSMUwTllLqmFVITyftralU79KZ7a++yqpu3TiUGbHaJVQ5pwlLKXVc4ipUoN7QoTQcPZpDy1ewsmNHfvvPf/wOS8UgfVu7UqpEVGl7BUmnNGfdoMGsG3AX+7p2pc69Q4irUMHv0I5NRhFvrF+1N7z+0puUTDxKz7CUUiUnsXFj0ia/QY2ePdkxeTKrOnfm0KpVfoelYoQmLKVUiZLEROrefx+NXnyRrHXrWXnd9ex6/wO/w1IxwLdLgiLSGHgNqAsYYKwx5plITS/t/pLdYDau2Fbi41014qoSG5dSfqvc+hKSZs1k3eB7WH/PPXRNTmZalcp+h6XKMD/vYWUBg40x34hIZeBrEZlrjFnmY0xKqWOVz72cBCD1RNiyvzIXOJC2dydZ91cnPqmQerYKueezaeYm9v20D4Afe/5I7Q61qdux7vFG7ptYm59I8y1hGWM2ABu837tFxAEaApqwVMFi7UZ4KcyP3ztFiYM6p+/mn9/9Rp+E+qz+tCZNLtlGQnLxK4es27FuTO3Qo2V+HMtuCzwDBIDxdtAZkU8/NwIZuFfEvrODThevfTbwg9fbGjvotI9UnFFRSlBE0oAzgP/l060v0BcgMTGxVOOKGbG2k48wv3fwJS1adopLc/bx/KF1DNnXkNWf1CL1kq0kpGiNxn5zLDsAvABcBmQCixzLnm0HnWUh/TQDHgDOt4PODsey64SMYr8ddFqURqy+JywRqQS8A9xtjPktb3djzFhgLEBKSoop5fBUORQtO/hY9Ks5QONW21j7eU1Wf1qLJpdsI7GS1rHls7OAX+2gswLAseypQAeOvtrVB3jBDjo7AOygs7nUo8TnUoIikoCbrCYbY2b4GYtSqnRUrHWYJpdsI/tQHKs/rcmh3QG/QyoP4kVkccinb0i3hsDakOZMr12ok4CTHMv+r2PZC7xLiLmSHMte7LW/NkLxAz4mLBER4BXAMcY87VccqmTkXkbb99M+fuz5I5tmbvI7JBXFkmscJrX1VkyWsPrTWhz8TZNWhGUZY1qGfMYWc/h4oBnQCugMjHMsu5rXLdUOOi2BLsBox7JPKLGo8wnCL+cD3YAfRGSJ1+7vxpg5PsakjpFeRlPFlVQ9iyatt7HmM/fyYOol26hQVevX8sE6oHFIcyOvXahM4H920DkMrHQs+2fcBLbIDjrrAOygs8Kx7Hm45RGWRyJQ386wjDH/Z4wRY8xpxpgW3keTlVLlSFK1LFLbbEOA1Z/W5MAO32+rl0eLgGaOZac7lp0IdAJm5+lnFu7ZFY5l18K9RLjCsezqjmVXCGl/PhEs6a1vulBK+apClSxS22xF4mD1Z7VI36Blq0qTHXSygH7AR4ADTLODzlLHsoc5lp1bRP0jYJtj2cuAz4AhdtDZBtjAYseyv/PajwgtXVjS9HBGKeW7xMrZpLbZyppPazJ0SjaP3hTg14bid1jlhh105gBz8rQbGvLbAIO8T2g/XwGnlkaMoGdYSqkokVgpm9Q229idDA9NzebktXqmpY6mCUspFTUSUrJ5+OYAOyvBg29l03y1PlisfqcJSykVVXZUFh7uGmBLVXhgWg6nrtSkpVyasJRSUWdXJeGfXQJsrA73vZ1Di+WatJQmLKVUlPotxU1ambVhyDs5/PkXTVrlnSYspVTU2lNRGNY5wKo6MHhGDmcHNWmVZ5qwlFJRbV+S8EjnAL/Wh7tn5XD+Uk1a5ZUmLKVU1NtfQXi0U4BgY+j/Xg4Xf69JqzzShKWUKhMOJgrDbwzwY6pw+wc5tF6iSau80YSllCozDiUIj/81ju+aCrf9O4fLv9akVZ5owlJKlSmHE4Qnr49jUTPh1v/k0G6hJq3yQhOWUqrMyYoXnu4Yx4KThZ6f5NBhviat8kATllKqTMoOCKOvjeO/ttB1Xg7X/58mrVinb2tXSpVZOXHCs+3jyArkcNOXOcRnG966KA5E3/QeizRhKaXKNBMnvHi1m7Su/8oQn5PD5FZ68SgWacJSSpV5RoSxV8aRFZdDhwWG+KwcTA+D6JlWTNHDEKVUTDAivHJFHB/8RbhqsWHjsGGYHL2vFUv0DEspFTtEmNTGvTzYYcpUzOHD1B82DInTY/NY4GvCEpG2wDNAABhvjBnhZzxKqRggwuRWcfQ+sy9bX3wJDmdR/7FHkUDA78iilmPZR+2L7aDzh32xY9k3AhmAAb6zg04Xr30P4CGvt0fsoDMpUnH6dtghIgHgBeBKoDnQWUSa+xWPUiqGiFB7wABqDejPrnffZf2QezGHD/sdVVRyLPsP+2LHspvn6acZ8ABwvh10TgHu9trXAB4GzgbOAh52LLt6pGL18zz5LOBXY8wKY8whYCrQwcd4lFIxpvYdd1DnnsH8NmcO6wbfgzl0yO+QotFZwK920FlhB52C9sV9gBfsoLMDwA46m732VwBz7aCz3es2F2gbqUD9vCTYEFgb0pyJm6WPIiJ9gb4AiYmJxzyxVSOuOuZh89NqwZMAzCvh8UZExq7Cu89r5fU3r9DefiiRYI5TLM0LxNb8ROm81Lz1ViQhgU3DR5B512EaPjOauHD2JVE6PxEQzr74JADHsv+Le9kwww46HxYwbMNIBRr1dyKNMWONMS2NMS3j47WMiFKq+Gr06EHdof9gz2efkXlnP3IOHPA7pNIWLyKLQz59izs80AxoBXQGxjmWXa2kgwwnCL+sAxqHNDfy2imlVImr0aULkpDAxqEPs/b222n84ovEJSf7HVZpyTLGtCygWzj74kzgf3bQOQysdCz7Z9wEtg43iYUOO68kAs6Pn2dYi4BmIpIuIolAJ2C2j/EopWJc9RtuoP7wx9j3v4Ws7fs3cvbu9TukaLAIaOZYdrpj2QXti2fhJSbHsmvhXiJcAXwEXO5YdnWvsMXlXruI8C1hGWOygH64M+cA04wxS/2KRylVPlS79loaPPEE+775hjW39iF7926/Q/KVHXT+sC+2g85Sx7KHOZbd3uvtI2CbY9nLgM+AIXbQ2WYHne3Av3CT3iJgmNcuIny9KWSMmQPM8TMGpVT5U/Xqq5CEBNYNHsya3rfSZNxYAlWr+h2Wb+yg84d9sR10hob8NsAg75N32AnAhEjHCGWg0IVSSkVClSsup9Gzz3DQcVjdqxdZO3b4HZIqgiYspVS5Vbl1axq98DyHfl3Omh49ydq2ze+QVCE0YSmlyrVKF11E4zEvcWjNGlb36MHhzZuLHkj5QhOWUqrcSznvPBqPfZnD6zewpnsPDm/a5HdIKh+asJRSCkg56yyajB9H1pYtrL65G4fX6WOh0UYTllJKeSqeeSZNJrxC9s6drO7WnUNr1xY9kCo1mrCUUipE8umn02Tiq+Ts3esmrVWr/A5JeTRhKaVUHsmnnEKTSRMxBw+yult3Di5f7ndICk1YSimVryTLIvW1SRhjWN29Bwd+/tnvkMo9TVhKKVWACs2akfraa0ggwJruPTiwbJnfIZVrmrCUUqoQFZqmk/r6a0hyMqt79mL/D2WghqsYpQlLKaWKkJiaSurrrxOoUoU1vW5h37ff+h1SuaQJSymlwpDYqCGpr79GoGYNfu3WnT0LF/L5558jImRkZPgdXrmgVfgqpVSYEurXJ/W111nTqxdvJCTQeMwYUs4+y++wyg09w1JKRVxGRgaff/55TJyRJNStQ+prk6h45pkk1K3jdzjliiYspaJULO3kMzIyMMYc+ZTleQGIr1WLJq+MJzEtze9QyhVNWCpmxNIOHmJvJ6/U8dKEdQxiaccYa/OiO3ilYpcYY/yOIWwpKSlm7969foehlFJliojsM8ak+B3H8dJSgkopVc45lt0WeAYIAOPtoDMiT/eewJNAbp0rz9tBZ7zXLRvIfZp6jR102kcqTk1YSilVjjmWHQBeAC4DMoFFjmXPtoNO3vdQvWUHnX75jGK/HXRaRDpO8Okelog8KSJBEfleRGaKSDU/4lBKKcVZwK920FlhB51DwFSgg88x5cuvQhdzgT8ZY04DfgYe8CkOpZQqD+JFZHHIp29It4ZAaE2VmV67vK53LPt7x7KnO5bdOKR9kmPZix3LXuBY9rWRCD6XLwnLGPMfY0yW17gAaORHHEopVU5kGWNahnzGFnP494A0O+ichnvCMSmkW6oddFoCXYDRjmWfUEIx/0E03MO6BXiroI7ekUDu0YARkf2lElXJiQeyiuyrbNB5iV6xND+xNC8QHfOTXEi3dUDoGVMjfi9cAYAddLaFNI4Hngjpts77XuFY9jzgDCAiNV5GLGGJyMdAvXw6PWiMedfr50HcFTm5oPF4RwLFPRqIGiKy2BjT0u84SoLOS/SKpfmJpXmBMjE/i4BmjmWn4yaqTrhnS0c4ll3fDjobvMb2gOO1rw7ss4POQceyawHnE5LMSlrEEpYx5tLCuotIT+BqoI0pSw+DKaVUDLGDTpZj2f2Aj3CLtU+wg85Sx7KHAYvtoDMbGOBYdnvcE4ztQM/cwYGXHcvOwb3FNCKf0oUlxpcHh0WkLfA0cLExZkupB1CKysDRVdh0XqJXbFbjYAAABvtJREFULM1PLM0LxN78+MmvUoLPA5WBuSKyRETG+BRHaSizlzPzofMSvWJpfmJpXiD25sc3ZerVTEoppcovffmtUkqpMkETllJKqTJBE1aEiEhbEflJRH4Vkfv9jud4iMgEEdksIj/6HcvxEpHGIvKZiCwTkaUicpffMR0rEUkSkYUi8p03L//0O6bjJSIBEflWRN73O5bjJSKrROT/27v3WCnKM47j35+CN0BtqrYQm0DSatMYRGIxKWiBENILbbyW0GhbbUiIJi01hKamSQn/NAZbKOkfpmlpIW04Ba2JWiVSQYoK5aYcpBijlia0CNp6AWMJl1//eN8lw7Lnwu5ZZgeeT7I5O7PvzDxzTk6enXnfed4duZ9+S9nxnAmiD6sNJJ1LKjl1vJgkMMN224Z7tpOkm4CDwDLb15QdTyskDQeG294maRiwFbi5in8bSQKG2D4oaTDwPPB92xtLDq1pku4Hrgcutj2t7HhaIWk3cL3td8qO5UwRV1jtMQ543fabtju6mGR/2P4r6dmLyrO91/a2/P4A6QHIRnXTOp6Tg3lxcH5V9huopCuBr5IqKYRwkkhY7dHfYpKhRJJGksrI/K3cSJqXb6G9DOwHVtuu7LkAi4C5wLGyAxkgBp6RtLWu2GxoUiSscFaSNBR4FJht+4Oy42mW7aO2x5Dqv42TVMlbtpKmAfttby07lgE0wfZY4MvAffnWemhBJKz26LOYZChP7u95FPiD7T+VHc9AsP0esBb4UtmxNGk88PXc79MFTJb0+3JDao3tf+Wf+4HHSF0FoQWRsNpjM/AZSaMknUcqJvl4yTEFjg9U+A2wy/bPy46nFZIur01+KulC0iCfV8uNqjm2f2T7StsjSf8va2zfWXJYTZM0JA/qQdIQYCpQ+VG2ZYuE1QZ5rq9aMcldwArbO8uNqnmSlgMbgKsl7ZH03bJjasF44C7SN/iX8+srZQfVpOHAWkndpC9Jq21Xfjj4GeITwPOStgObgD/bXlVyTJUXw9pDCCFUQlxhhRBCqIRIWCGEECohElYIIYRKiIQVQgihEiJhhRBCqIRIWKFjSPqkpC5Jb+RyNk9Juuo0xzBR0hcKy7MkfWsA9nuOpMWSXskVvDdLGtXqfvs45nxJU/L72ZIuaufxQmi3GNYeOkJ+oPdFYKnth/O6a0lVu9f3Y/tB+fm3hsunEMc84KDth0512z72OwO4DfiG7WO50OuHtt8dyOMUjneu7aOF5d1E5fBQcXGFFTrFJOBwLVkB2N5ue72SBYWrk+lw/GpovaTHgb83WB5ZnMNL0pyckJD0nKRf5AeHX5E0LhfDnQX8IK+/UdI8SXPyNmMkbZTULekxSR8r7OvBPDfVa5JubHB+w4G9to/lc9tTS1aSpkraIGmbpJWShirNp7ayEPvE2hxRjdrn9btzHNuAOyT9TtLtkr4HjCA9ZLxW0j2SFhX2PVPSwpb+eiGcBpGwQqe4hjQ3VSO3AmOAa4EpwAKlea0AxpLmgLqqh+XeXJQLx94LLLG9G3gYWGh7TIMru2XAD22PBnYAPyl8Nsj2OGB23fqaFcDXciL8maTrACRdBvwYmJILpW4B7gf+AtyQy/oATAe6emlf8x/bY2131VbYXgz8G5hke1IhlsG5yd3Akn78vkIo1aCyAwihHyYAy/Mtrn2S1gGfBz4ANtn+R6Ft/XJvlkOa70vSxbW6fI1IugS41Pa6vGopsLLQpFZEdyswsn5723skXQ1Mzq9nJd0BXAh8Dngh3RXlPGCD7SOSVpESyyOkeaLmAl9s1L5wqD/2ddJ5wsc1wDRJu4DBtnf0tV0IZYuEFTrFTuD2Jrb7sJflI5x4F+GCurb1HbitdOgeyj+P0sP/le1DwNPA05L2ATcDz5BqAM5osEkXqSblf4Ettg/kvr6e2sPJv4+e/Bp4gFQs97f93CaEUsUtwdAp1gDnqzDRnaTRuT9oPTBdabLCy4GbSAVF+7IPuELSxyWdD9RPuV7rC5sAvG/7feAAMKx+R/mzdwv9U3cB6+rb9UTSWEkj8vtzgNHAP4GNwHhJn86fDSmMjFxHusU5k5S86KN9b044rzzR46eAb5KvNEPodJGwQkdwGq56CzBFaVj7TuCnwFukuYS6ge2kxDbX9lv92OdhYD4pua3m5Kk3/ifpJVK/Va0C/RPALbVBF3Xtv03qP+sm9anNP4VTvAJ4Ig8C6SZd/f3S9tvAd4Dleb8bgM/m+I8CT5ImAHwyr+uxfR9+BayStLawbgXwQrtGKoYw0GJYezgrSXoOmGN7S9mxlCWPOlxo+9myYwmhP+IKK4SzjKRLJb0GfBTJKlRJXGGFEEKohLjCCiGEUAmRsEIIIVRCJKwQQgiVEAkrhBBCJUTCCiGEUAn/BzdpDtCkgg2lAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_labels = ['0', '1', '2', '3', '4', '5']\n",
    "\n",
    "N = 6\n",
    "ind = np.arange(N)\n",
    "width = .35\n",
    "\n",
    "fig_bar_cd, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "p0 = ax.bar(ind[0], score_x.mean(), yerr=score_x.std(), capsize=2)\n",
    "p1 = ax.bar(ind[1:], mu_noharm, width, yerr=std_noharm, capsize=2)\n",
    "p2 = ax.bar(ind[1:] + width, mu_harm, width, yerr=std_harm, capsize=2)\n",
    "\n",
    "ax.set_title('Harmfullness Scores and Accuracy by Corruption Severity')\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(plot_labels)\n",
    "ax.set_ylim((-2))\n",
    "ax.legend((p1[0], p2[0]), ('Not Harmful', 'Harmful'), loc='upper right', ncol=2)\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_xlabel('Corruption Severity')\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Accuracy', color=color)\n",
    "ax2.plot(acc, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot p-values for contaminated batches\n",
    "\n",
    "In order to simulate a realistic scenario, we perform a K-S test on batches of instance which are increasingly contaminated with corrupted data. The following steps are implemented:\n",
    "\n",
    "* We randomly pick `n_ref=1000` samples from the non-currupted test set to be used as a reference set in the initialization of the K-S drift detector.  \n",
    "\n",
    "* We sample batches of data of size `batch_size=100` contaminated with an increasing number of harmful corrupted data and harmless corrupted data. \n",
    "\n",
    "* The K-S detector predicts whether drift occurs between the contaminated batches and the reference data and returns the p-values of the test.\n",
    "\n",
    "* We observe that contamination of the batches with harmful data reduces the p-values much faster than contamination with harmless data. In the latter case, the p-values remain above the detection threshold even when the batch is heavily contaminated\n",
    "\n",
    "We repeat the test for 100 randomly sampled batches and we plot the mean and the maximum p-values for each level of severity and contamination below. We can see from the plot that the detector is able to clearly detect a batch contaminated with harmful data compared to a batch contaminated with harmless data when the percentage of currupted data reaches 20%-30%.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for s in severities:\n",
    "    nrows = 1\n",
    "    ncols = 2\n",
    "    figsize = (15, 8)\n",
    "    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)\n",
    "    title0 = ('Mean p-values for various percentages of corrupted data. \\n' \n",
    "             ' Nb of batches = {}, batch size = {}, severity = {}'.format(\n",
    "                 nb_batches, batch_size, s))\n",
    "    title1 = ('Maximum p-values for various  percentages of corrupted data. \\n' \n",
    "             ' Nb of batches = {}, batch size = {}, severity = {}'.format(\n",
    "                 nb_batches, batch_size, s))\n",
    "    dfs[s][['pvalue_harm_mean', 'pvalue_noharm_mean']].plot(ax=ax[0], title=title0)\n",
    "    dfs[s][['pvalue_harm_max', 'pvalue_noharm_max']].plot(ax=ax[1], title=title1)\n",
    "    for a in ax:\n",
    "        a.set_xlabel('Percentage of corrupted data')\n",
    "        a.set_ylabel('p-value')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
